SAR image despeckling with a multilayer perceptron neural network

Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling.     

特大型桥梁施工GPS高程控制网技术研究

探索了采用GPS技术建立桥梁施工高程控制网的难点及关键问题,分析了GPS跨河高程传递测量方法的技术要点,结合工程实例得出了一些结论和建议。     

Application of probabilistic neural network to design breakwater armor blocks

This study presents a probabilistic neural network (PNN) technique for predicting the stability number of armor blocks of breakwaters. The PNN is prepared using the experimental data of Van der Meer. The predicted stability numbers of the PNN are compared with those of previous studies, i.e. by an empirical formula and a previous neural network model. The agreement index between the measured and predicted stability numbers by PNN are better than those by the previous studies. The PNN offers a way to interpret the network's structure in the form of a probability density function and it is easy to implement. Therefore, it can be an effective tool for designers of rubble mound breakwaters.     

Matlab在矿区GPS网间接平差中的应用

针对矿区GPS网间接平差,首先,详细地分析了GPS网间接平差原理,按一定格式将GPS网中的观测数据编制成观测文件,其次,按观测文件的格式编制算法将数据读入并存储,然后用间接平差原理处理得到平差结果,最后绘制网形和误差椭圆并生成平差报告,为矿区控制网的建立和矿区生产管理提供了便利。实验验证了本文程序的实用性。     

GPS技术在金川露天矿边坡变形监测中的应用

本文结合金川露天矿边坡变形GPS监测研究,主要论述监测基点的选择,基点WGS-84坐标的确定,变形监测网的布设,GPS数据采集、处理和质量,坐标转换的实施等,给出反演推出的若干四等三角点及其它控制点的变形数据,这些数据和造成变形的物理成因符合得很好,最后概括出GPS技术应用于变形观测的若干优点。     

Toward an improved global network for determination of tropospheric ozone climatology and trends

An examination of typical tropospheric ozone variability on daily, monthly, annual and interannual timescales and instrumental precision indicates that the current ozonesonde network is insufficient to detect a trend in tropospheric ozone of 1% per year at the 2 level even at stations with records a decade in length. From a trend prediction analysis we conclude that in order to detect a 1% per year trend in a decade or less it will be necessary to decrease the time between observations from its present value of 3–7 days to 1 day or less. The spatial distribution of the current ozonesonde stations is also inadequate for determining the global climatology of ozone. We present a quantitative theory taking into account photochemistry, surface deposition, and wind climatology to define the effectively sampled region for an observing station which, used in conjunction with the instrumental precision and the above prediction analysis, forms the basis for defining a suitable global network for determining regional and global ozone climatology and trends. At least a doubling of the present number of stations is necessary, and the oceans, most of Asia, Africa, and South America are areas where more stations are most needed. Differential absorption lidar ozone instruments have the potential for far more frequent measurements of ozone vertical profiles and hence potentially more accurate climatology and trend determinations than feasible with ozonesondes but may produce a (fair weather) biased data set above the cloud base. A strategy for cloudy regions in which either each station utilizes both lidars and sondes or each station is in fact a doublet comprised of a near-sea-level lidar and a proximal-mountain-top lidar could serve to minimize this bias.     

特种监测网解算中IGS站选取的探讨

利用GAMIT/GLOBK软件处理2011年特种监测网的观测数据,将IGS站的选取分10种方案进行试验,计算结果从基线长度变化、测站坐标、NRMS、基线重复率等方面进行了比较。得出利用GAMIT/GLOBK软件处理该特种监测网时的几点建议:建议选取IGS站为GPS网提供参考框架;IGS站选取的数量不一定最多,但空间上应尽量均匀分布;选取7个IGS站时得到较高精度的结果。在日后处理此类型的特种监测网时对IGS站选取具有一定的参考价值。     

一种基于双通道的水下图像增强卷积神经网络

近年来各国对于海洋生物的保护意识日益强烈,用来监测海洋生物生存状态的水下机器人装备的研发是保护海洋生物资源的关键。水下相机是这类机器人在水下进行海洋生物监测时的光学感知设备。然而水下环境复杂,拍摄到的图像模糊不清,为解决水下图像模糊等问题,提出了一种基于双通道的水下图像增强卷积神经网络。在网络的编码器中采用双通道结构,其中一个通道采用了密集连接和高效通道注意力机制,提取水下图像的细节特征,另一个通道采用多尺度结构,提取原始图像的多尺度语义特征。接着,在网络中引入残差注意力模块和自适应特征融合模块,进一步优化了特征。最后将优化后的特征输入解码器重建出增强后的水下图像。试验表明：提出的网络算法在UIQM指标和Entropy指标上分别为3.005 6和7.654 7,较第二名的算法分别高出0.097 5和0.123 2。     

System identification for nonlinear maneuvering of large tankers using artificial neural network

This paper deals with the application of nonparametric system identification to a nonlinear maneuvering model for large tankers using artificial neural network method. The three coupled maneuvering equations in this model for large tankers contain linear and nonlinear terms and instead of attempting to determine the parameters (i.e. hydrodynamic derivatives) associated with nonlinear terms, all nonlinear terms are clubbed together to form one unknown time function per equation which are sought to be represented by the neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag maneuvers and the proposed method has been applied to these data. The neural network scheme adopted in this work has one middle or hidden layer of neurons and it employs the Levenberg–Marquardt algorithm. Using the best choices for the number of hidden layer neurons, length of training data, convergence tolerance etc., the performance of the proposed neural network model has been investigated and conclusions drawn.     

基于网络药理学探讨附子-细辛对高血压病的作用机制

目的：基于网络药理学研究附子-细辛对高血压病的作用机制。方法：运用TCMSP数据库挖掘出附子-细辛的有效成分及药物预测靶点,采用5个生物信息数据库检索出高血压病的疾病预测靶标,构建药物靶点-疾病靶标交集,使用Cytoscape生物信息分析软件绘制“成分-靶点-疾病”中药调控网络,基于STRING数据库构建“药物-疾病”相关靶点的蛋白互作网络（PPI）,并对其进行GO和KEGG的生物信息学分析。结果：通过生物利用度和类药性筛选得到附子-细辛的有效成分29个,对应药物靶点1807个,高血压病的疾病靶标8505个,构建的药物靶点-疾病靶标交集80个。与高血压病密切相关的GO富集条目91个,KEGG信号通路123条,主要涉及流体剪切应力与动脉粥样硬化、糖尿病并发症中的AGE-RAGE信号通路、白细胞介素（IL）-17信号通路、丝裂原活化蛋白激酶（MAPK）信号通路、PI3K-Akt信号通路等。结论：附子-细辛药对与高血压病密切相关,能够通过一条或多条通路协同发挥药效作用,考虑其可能是通过干预血液的流体剪切应力影响动脉粥样硬化与心血管重塑再生而实现的。